In analysis of medical imaging results, such as magnetic resonance (MR) images and/or computed tomography (CT) images, it is often desirable to provide quantitative results, such as quantitative perfusion maps. To provide such results, identification of arterial input function (AIF) and venous output function (VOF) regions of images is typically required. Manual identification of such regions is time consuming and subject to operator preference and operator variation. Automatic AIF/VOF identification has been considered. However, conventional automated methods tend to be insufficiently robust in the presence of patient motion and/or noise. Accordingly, it would be an advance in the art to provide improved automated AIF/VOF identification.